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Service Differentiated Provisioning in Dynamic WDM Networks Based on Set-Up Delay Tolerance Ajmal Muhammad, Cicek Cavdar, Lena Wosinska, and Robert Forchheimer
Abstract—Optical networks are expected to provide a unified platform for a diverse set of emerging applications (three-dimensional TV, digital cinema, e-health, grid computing, etc). The service differentiation will be an essential feature of these networks. Considering the fact that users have different levels of patience for different network applications, referred to as set-up delay tolerance, it will be one of the key parameters for service differentiation. Service differentiation based on set-up delay tolerance will not only enable network users to select an appropriate service class (SC) in compliance with their requirements, but will also provide an opportunity to optimize the network resource provisioning by exploiting this information, resulting in an improvement in the overall performance. Improvement in network performance can be further enhanced by exploiting the connection holding-time awareness. However, when multiple classes of service with different set-up delay tolerances are competing for network resources, the connection requests belonging to SCs with higher set-up delay tolerance have better chances to grab the resources and leave less room for the others, resulting in degradation in the blocking performance of less patient customers. This study proposes different scheduling strategies for promoting the requests belonging to smaller set-up delay tolerance SCs, such as giving priority, reserving some fraction of available resources, and augmenting the research space by providing some extra paths. Extensive simulation results show that 1) priority in the rescheduling queue is not always sufficient for eradicating the degradation effect of high delay tolerant SCs on the provisioning rate of the most stringent SC, and 2) by utilizing the proposed strategies, resource efficiency and overall network blocking performance improve significantly in all SCs. Index Terms—Connections holding-time; Deadline driven provisioning; Dynamic connection provisioning; Dynamic scheduling; Set-up delay tolerance; WDM networks.
I. INTRODUCTION
W
avelength division multiplexing (WDM) technology provides a high capacity transport infrastructure for today’s core and metro optical networks. In a WDM network, a lightpath must be established between a pair of Manuscript received March 18, 2013; revised August 4, 2013; accepted September 4, 2013; published October 29, 2013 (Doc. ID 187080). A. Muhammad (e-mail:
[email protected]) and R. Forchheimer are with Linköping University, Linköping, Sweden. C. Cavdar and L. Wosinska are with the Royal Institute of Technology (KTH), Stockholm, Sweden. http://dx.doi.org/10.1364/JOCN.5.001250
1943-0620/13/111250-12$15.00/0
source and destination nodes before data can be transferred. A lightpath is an end-to-end optical connection that traverses multiple fiber links and optical nodes, e.g., cross-connects (OXCs). Setting up a lightpath for a connection request by using the routing and wavelength assignment (RWA) technique [1] is referred to as connection provisioning. The emergence of new applications, such as video on demand (VoD), distribution of ultrahigh definition TV (UHDTV), three-dimensional TV (3DTV), digital cinematic production, interactive gaming, e-health, e-science, cloud computing, banking data backup storage, and grid computing, to mention a few, are pushing WDM to expand toward access networks [2,3]. It is envisioned that optical networks equipped with agile devices, such as reconfigurable optical add–drop multiplexers (ROADMs) and tunable transceivers integrated with G-MPLS/ASON control-plane technology will be extended to all premises [4]. Such a “single wavelength per user” network is conceivable as a future network where the number of wavelength channels may be orders of magnitude larger than today’s networks. This will provide a unified platform for the above-mentioned applications that hold the salient features of being usercontrolled, bandwidth-intensive, and of relatively short but known duration. A key objective of such networks is to not only fulfill the bandwidth requirement of these applications, but also to offer different grades of service, as was argued in [5,6]. Several potential service differentiation parameters for WDM networks are proposed in [7]. Among these parameters, set-up time [8,9] and survivability [10,11] based service differentiation have so far attracted researchers’ interest. Connection set-up time, which is defined as the amount of time between the instant a connection request is received and the moment the request is provisioned in the network, is often determined by the service provider. However, another term, namely, set-up delay tolerance is defined in [12], which describes a customer’s patience, i.e., the maximum duration a customer is willing to wait until the connection is set up. Contrary to connection set-up time, the set-up delay tolerance (td ) is defined/ decided by the customer. Furthermore, connection holding time (th ) describes the duration for which a connection remains active and occupies network resources. It is expected that the th of connection requests can be known in advance, as it is considered to be a specification defined in © 2013 Optical Society of America
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the service level agreement (SLA) between the service provider and its customers [13]. Considering the rapid exhaustion of optical fiber physical capacity [14], new approaches that make the most out of scarce network resources and allow accommodating everincreasing traffic demands need to be developed. There are several approaches available in the literature addressing the connection provisioning problem for the abovementioned set of demanding applications. Among them, the strategies exploiting the time dimension of a connection request, i.e., td and th , have proven to be particularly effective [12,15–18]. td improves connection provisioning by rescheduling the connection request for a series of provisioning attempts, within a predefined amount of waiting time, when the required network resources are not free. Similarly, if the th of the already established connection requests are known in advance, some insight about the network state for upcoming time frames can be gained. This information, which enables more efficient use of network resources will consequently reduce the connection blocking probability. It is obvious that td and th depend on the applications. For instance, online trading and stock market applications are expected to demand more stringent td compared to database backup applications [8]. Inspired by this observation, this work considers three service classes (SCs) in terms of set-up delay tolerance, where each SC, namely, SC-I, SC-II, and SC-III, tolerates a different value of td. Previous studies [9] addressing td focus on the overall network performance. However, we found that when multiple classes of service are considered, the blocking performance of some SCs may not always be as good as the overall network blocking ratio. The class of connection requests with a smaller td constraint might show degraded performance while the total performance improves. This is because the connections with longer td are more likely to grab resources compared with impatient requests. The objective of the study presented in this paper is twofold: first, to improve the resource efficiency and connection provisioning ratio for differentiated classes by using td and th aware strategies to provision bandwidth intensive (i.e., one wavelength of capacity) connection requests. Second, this study aims to distribute the gain in terms of network connection provisioning fairly among each SC. In order to promote the higher demand classes over the lower ones, different priority queuing techniques are used and connection requests are dynamically scheduled within their td. In the proposed RWA solution, the routing and wavelength assignment subproblems are treated separately. Precomputed k-shortest paths between each source destination pair are considered for routing, while for wavelength assignment the first-fit scheme [19] is employed. Furthermore, a central controller is assumed to keep track of the network state, which is responsible for selecting route and wavelength for the connection request and sending control signals to appropriate nodes for establishing and releasing lightpaths.
An early stage of this study was presented in [20] without detailed performance analysis, while this paper analyzes the impact of several network parameters (e.g., network connectivity, traffic distribution among the SCs) on the blocking performance of the proposed strategies. The rest of the paper is structured as follows. A brief overview of related work and motivation for differentiated connection provisioning are presented in Section II. Section III introduces the assumptions for differentiated connection provisioning. The problem statement and notations are described in Section IV. Strategies for combining td and th awareness for on-demand provisioning of connection are explained in Section V. Proposed algorithms for differentiated connection provisioning are presented in Section VI. The time complexity analysis of the proposed algorithms is presented in Section VII. Simulation results are shown in Section VIII, and, finally, some concluding remarks are made in Section IX.
II. RELATED WORK The idea of provisioning connection requests with flexible start times instead of provisioning them at the moment of arrival has been studied in the literature for around a decade. Different terminology has been used for the work that utilizes the time flexibility. This section presents a brief overview of the strategies for the dynamic traffic scenario. Starting with the most common term, namely, advance reservation (AR), which was defined first in [21], in which the authors consider allocation of resources in advance before the connection is actually set up. The time interval between reservation and utilization of network resources is called the book-ahead time. Different models for AR, based on whether the connection set-up time is fixed or flexible and the connection th is known or not, are described in [22]. Moreover, the authors in [12,15] introduce a new term, delay tolerance, which describes customer patience, i.e., the maximum duration a customer is willing to wait until the connection is set up. Contrary to book-ahead time, which is typically greater than connection th , the delay tolerance is assumed to be a fraction of the connection th , except for non-real-time applications. For the fixed connection set-up time and known th model (introduced in [22]), the connection set-up and tear-down times are fixed. The studies in [21,22] present scheduling strategies for fixed connection set-up time and a known th model, assuming that each connection request needs one wavelength of capacity. For RWA they use precomputed k-shortest path routing and random wavelength assignment. Similarly, [23] addresses the same problem for the case when the connection request requires multiple wavelengths. The authors in [24,25] propose lightpath migration strategies for fixed connection set-up time and known th connection requests in order to schedule a newly arrived request. The proposed strategies reassign network resources to the scheduled but not yet active connection requests. The aims of these strategies are to accommodate the new request in the network and to reassign resources efficiently.
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The flexible connection set-up time and known th model allows the set-up time to slide within a time window, but a deadline is set for the end of the th , so the method is described as deadline driven [26]. For this model, the authors in [27] propose scheduling strategies for distributing data from multiple sources to a single destination. They consider a network with opaque OXCs and grooming facility. The same authors devise algorithms for dynamic bandwidth allocation in [26,28,29] for flexible set-up time and known th anycast requests. The algorithms retain maximum resources unused to accommodate future traffic. The studies in [30–32] investigate ligthpath migration schemes for flexible connection set-up time and known th connections. The proposed schemes calculate new RWA for the scheduled (but not yet active) connection requests in order to provision the newly arrived request and to keep the network in an optimal state. The studies in [12,15] present strategies for flexible set-up time that exploit the td and search for opportunities to provision an otherwise blocked connection when a connection departs from the network. The proposed strategies employ the flexibility in set-up time to efficiently utilize the resources for survivable networks. Moreover, the strategies make use of the request’s arrival time, td , and th as parameters for prioritization of connection requests that are waiting in the queue for provisioning. The authors in [9] present a scheduling strategy that gives priority in the waiting queue to connection requests with smaller td. The network connection requests are divided into three SCs with different td s, and connections belonging to the SC with smaller td are always given priority in the waiting queue.
(BP). When multiple classes of services with diverse td values are considered, the SC with smaller td might not always benefit from queue priority. Connections belonging to such an SC may suffer from the higher connection acceptance rate of other SCs due to their high td . Therefore, strategies other than priority queuing need to be developed.
Some studies, for instance [33,34], look into the flexible connection set-up time with unlimited sliding. In this case, instead of blocking a request that cannot be scheduled within a given set-up time, the request set-up time can slide for an unspecified amount of time outside its given set-up time until it can be scheduled.
Service differentiation provides a valuable opportunity for service providers to raise their revenue by selling high-added-value services. Different classes of service, namely, premium, gold, silver, and bronze, with different set-up time specifications have been proposed in [7]. However, this study addresses the service differentiation issue from a network operator perspective depending on the ability of the operator to set up the connection.
For connection requests with instant provisioning demand and known th , prior knowledge of connections th has been exploited for different scenarios. For example, the authors in [17,35,36] exploit th awareness for efficient utilization of lightpath capacity and bandwidth allocation for the traffic grooming problem, while [12,37] make use of th information for enhancement of backup resource sharability in survivable networks. Finally, the works reported in [18,38,39] utilize th knowledge to distribute the load among the network links efficiently and to reduce the network congestion. To the best of our knowledge, only [9] investigated the issue of service differentiation in terms of td. The proposed strategies employ the idea of giving queue priority to connection requests with smaller td. However in [9], they assume that td values for the SCs are not so diverse (i.e., the td ratio is 3∶5∶7) and are independent of connections th . Furthermore, the strategies do not exploit the td awareness. None of the previous studies examine how the blocking performance of the SC with stringent td would be affected by the improvement of the overall blocking probability
This paper analyzes the problem of dynamic on-demand provisioning of optical channels for bandwidth-intensive and user-controlled applications. For this purpose, the network applications are coarsely divided into three different SCs, namely, SC-I, SC-II, and SC-III, where the user tolerance to get connected is influenced by the type of service requested by the user. In this work td is considered together with the th within the SCs since considering both metrics gives more specific information for the occupancy of resources. In order to correlate these two parameters, the td to th (td ∕th ) ratio is used and different numerical values of td ∕th, i.e., 0.01, 0.5, and 3, are assigned to SC-I, SC-II, and SC-III, respectively. Several provisioning strategies that in different ways take advantage of the combined effect of using td and the th awareness concept are proposed. The objective of this study is to reduce the overall BP of the network as well as to keep the BP of less tolerant SC connections below or equal to that of a non-delay-tolerant system (NDT) and improve the connection provisioning of the overall network.
III. SERVICE DIFFERENTIATION WITH SET-UP DELAY TOLERANCE
th and td for different services have been investigated only to a limited degree. Here we discuss such investigations while complementing them with reasonable assumptions. The aim is to see if the future applications can be clustered into a few groups, which can form the basis of our proposal for differentiated services. The future network applications will be very diverse in terms of th and td . Examples of future applications are online gaming, real-time services such as video and audio streaming, backup storage, telemedicine, e-science, grid computing, etc. Telemedicine, particularly remote surgery and interactive video and audio, are examples of demanding (i.e., smaller td ) real-time services. Some less demanding applications are VoD and large file transfers generated by e-science applications [40]. Another example for a non-real-time application is backup storage services. All these applications will have different values for the th and td . Starting with real-time applications, it is known
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that a connection set-up request for online gaming requires a very fast response. The th varies in a range of minutes. However, the td is in the seconds region due to the “hard real-time” constraints of these applications. Similar real-time arguments about th and td can be posed for remote surgery, although its th is likely to be much longer. Another time critical application is real-time entertainment such as audio and video streaming. For some of these the td is of the order of seconds [41] and the th of the order of minutes. For VoD, the th ranges from 30 to 120 min [42]. We assume that td up to about a minute can be accepted since streaming video services employ an initial playback delay of up to several tens of seconds [43] in order to ensure jitter free streaming. Other real-time applications are teleconferencing and telephony. In these cases it can be assumed that the td may be in the range of seconds to minutes, while the th varies from minutes (telephony) to hours (teleconferencing). Some of these applications (i.e., telephony) may demand very strict td , while others (i.e., teleconferencing) might require less stringent td . Finally, considering non-real-time applications, such as e-science applications [40], and backup storage, the td may extend to hours with similar values for the th . Summarizing the discussion with suggested values for the th and td for the various applications, we can coarsely classify these applications into three SCs, namely, SC-I, SC-II, and SC-III. SC-I comprises “hard real-time” applications with a small td to th ratio (td ∕th ), such as telephony, online gaming, and remote surgery. SC-II consists of less time critical applications, such as video and audio streaming, while SC-III contains “non-real-time” applications with high td , such as critical data backup, e-science bulk data transferring, and grid computing where there is bigger advance reservation time. Categorization of different applications in these different SCs according to their td to th ratio are shown in Table I. It should be observed that we are not attempting to define strategies that reflect the three classes. Rather, the goal is to see how the proposed strategies influence the quality of the classes. Finally, lacking knowledge about the traffic mix of future applications, we will resort to some preselected proportions of SC traffic.
IV. PROBLEM STATEMENT
AND
NOTATIONS
We consider a dynamic WDM network environment with connection requests from three SCs, each request requiring capacity corresponding to one wavelength. The service differentiated connection provisioning with delay tolerance (PDT) problem can be formulated as follows. Given: a) A physical topology of a network represented by a graph G with a set of links E and nodes V; maximum number of wavelengths on each link denoted by W; b) a connection request R fs; d; c; ta ; th ; td g between a source destination pair fs; dg of class c having arrival time ta , holding time th , and delay tolerance td ; c) a set of k precomputed shortest paths between fs; dg denoted by π sd;k.
DELAY TOLERANCE Service Class SC-I SC-II SC-III
TABLE I HOLDING-TIME RATIO SERVICE CLASSES
TO
FOR
Example of Applications
DIFFERENT td ∕th
Telecommuting, online gaming, 0.01 telemedicine Video and audio streaming 0.5 Backup storage, e-science, grid computing 3
Find: A connection i, Ci fwil ; pi ; tis g is set up on wl ∈ W on links L of path p ∈ π sd;k with a set-up time ts such that ts ≤ td − ϵ, where ϵ1 is a small constant. Objective: Minimize the total BP subject to the constraint that none of the SC BPs gets larger than the case when no set-up delay tolerance is utilized.
V. ALGORITHMS FOR PROVISIONING WITH SET-UP DELAY TOLERANCE AND HOLDING-TIME AWARENESS Network connection provisioning and consequently network BP can be improved significantly by exploiting the customer-oriented SLA metrics, such as td and th awareness. These metrics can be employed in different ways in terms of whether the resources are reserved in advance or not for the rescheduled connection requests to get provisioned.
A. PDT Without Resource Reservation PDT without resource reservation (PDT-WoR) reschedules all requests if they cannot be set up at their arrival due to resource unavailability, as shown in Fig. 1. Without reserving any resources, connection requests are rescheduled after the departure of an already provisioned connection within time td of the connection request ordered by their arrival times. Hence, the PDT-WoR is a departure-driven scheme, and attempts are made to provision not only the first (in the rescheduling queue) request but also all the rest of the requests in the rescheduling queue when a connection departs from the network. This is in contrast to [9], which does not attempt to provision the rest of the requests in the rescheduling queue once the preceding request (in the queue) fails to get connected, although they remain in the queue till each td expires. A first in, first out (FIFO) principle is utilized to provision the rescheduled requests once network resources are released by departing connections. Algorithm 1 describes the basic steps for PDT-WoR. Note that a connection is dropped from the rescheduling queue if a connection request cannot be set up within its td , and the queue is then reordered again.
1
ϵ is introduced to account for the actual set-up time of the connection.
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Fig. 1. PDT-WoR strategy.
Algorithm 1 PDT Without Resource Reservation (PDT-WoR) Input: GV; E, W, π sd;k , R fs; d; c; ta ; th g. Output: Connection C, Delay or Block. 1: Compute w free wavelengths; d on π sd;k . If w is available then C is set up. 2: Else find the expiration time (te ) of first departing connection de: de de tde e ts th R If tde < t tR e a d ϵ then R Update arrival time (tR a ), delay tolerance (td ) of R: de R R R R tmp te ϵ, td ta td − tmp, ta tmp, DELAY. Else BLOCK.
provisioned on the reserved resources as soon as those resources become free.
Algorithm 2 PDT With Resource Reservation (PDT-WR) Input: GV; E, W, π sd;k , R fs; d; c; ta ; th g. Output: Connection C, Reservation or Block. 1: Compute w free wavelengths; d on π sd;k . If w is available then C is set up. 2: Else compute D connections with expiration time (te ) less than tR d . If D 0 BLOCK. Else release2 all connections pertaining to D and re-compute w. If w is available RESERVE it for R. Else BLOCK.
A. Dif-PDT-{WoR/WR}
B. PDT With Resource Reservation The PDT with resource reservation (PDT-WR, Algorithm 2) strategy reserves resources while a connection request is rescheduled. This is done by using the information about the th of the already provisioned requests. PDT-WR computes the set D of all connections that will be released within td . If D 0, or if it is not possible to satisfy the resource requirement of the connection request even after all connections in D have left the network, then the connection request is immediately blocked. Otherwise, PDT-WR reserves in advance the required resources, and as a result, there is a time gap between the resource allocation phase (RAP) and the provisioning phase (PP). The connection is
2
The “release” operation is only conducted in the algorithm. It does not imply release of the physical lightpath.
VI. ALGORITHMS FOR SERVICE DIFFERENTIATED PROVISIONING WITH SET-UP DELAY TOLERANCE AND HOLDING-TIME AWARENESS In this section, we describe a set of strategies for service differentiated provisioning with td and th awareness (Dif-PDT). The objective of these strategies is to promote the connection requests with smaller td in order to balance the provisioning success rate among different classes.
The Dif-PDT-WoR employs the basic concept of PDTWoR. However, to promote SC-I connection requests it gives them priority in the rescheduling queue over SC-II and SC-III requests irrespective of their time of arrival in the queue. Whenever an existing connection departs the network, resources are released. Consequently, the Dif-PDT-WoR will attempt to provision SC-I requests (if any exist) first and subsequently SC-II and SC-III requests. If a SC-I request fails to utilize the released resources and get connected, then the resources will be allocated to other low-priority requests (i.e., SC-II or SC-III) if their paths are disjoint from that of SC-I request. The disjoint condition might avoid the possibility of the partial available resources for SC-I requests (in the queue) being utilized by SC-II and SC-III connections. Moreover, eliminating this condition might result in some deterioration of SC-I connection provisioning. Note that within connection requests belonging to the same SC, priority in the rescheduling queue is given to the request that arrives earlier in the queue. Similarly, the Dif-PDT-WR makes use of the PDT-WR concept in order to promote connections with low td. As mentioned in Section V (PDT-WR), there is a time gap between RAP and PP in PDT-WR strategy. Therefore, resources reserved by high set-up delay tolerant SC connection requests can be reallocated in favor of low delay tolerant request if the requests are not yet provisioned. In order to promote SC-I requests over the others, resources reserved for SC-II and SC-III connection requests are
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released and rescheduled if needed by SC-I connection requests. In the same way, resources reserved for SC-III connection requests are released in favor of SC-II connection requests. After reallocation, the connection belonging to a lower class is rescheduled. This request will be blocked if resources are not available within its remaining td .
B. Dif-PDT-{WoR/WR} With Wavelength Reservation Dif-PDT-WoR and Dif-PDT-WR might not always eliminate the adverse effect of higher td of SC-II and SC-III connection requests on the connection acceptance of SC-I. Thus, Dif-PDT-{WoR/WR} with wavelength reservation (Dif-PDT-{WoR/WR}-wr) not only gives priority to SC-I connection requests in the rescheduling queue, but also reserves a specific number (T) of wavelengths on each link e ∈ E for SC-I connection requests. The algorithm divides all the wavelengths W on each link into two pools. Wavelengths fw1 wn−T g are common wavelengths that can be used by all SCs, while fwn−T1 wn g are reserved wavelengths that will be used by SC-I connection requests only. With the first-fit scheme [19] this is achieved by using the wavelengths with the highest indices for the pool reserved for SC-I. A SC-I connection request will first try to use the common and then the reserved wavelengths as the last resort. For example, for W 8 and T 2 the Dif-PDT{WoR/WR}-wr strategy will have common wavelengths fw1 w6 g and reserved wavelengths fw7 ; w8 g for SC-I connection requests. Note that Dif-PDT-WoR-wr differs from Dif-PDT-WoR because it is additionally reserving wavelengths for SC-I. The same argument also applies for both the Dif-PDT-WR-wr and Dif-PDT-WR cases.
C. Dif-PDT-{WoR/WR} With Different Path Sets The aim of the Dif-PDT-{WoR/WR}-dp strategy is the same as that of Dif-PDT-{WoR/WR}-wr, that is, to further promote SC-I connection requests. The Dif-PDT-{WoR/ WR} with different path sets (Dif-PDT-{WoR/WR}-dp) algorithm adjusts the value of k, i.e., precomputed shortest paths to a larger value than that for SC-II and SC-III connection requests. The set of paths used for SC-I is π sd;u and for SC-II and SC-III π sd;v , such that π sd;v is a subset of π sd;u with u > v. Note that, when the different path sets are used in the case of the DIF-PDT-WoR algorithm, then it will be denoted by Dif-PDT-WoR-dp, while Dif-PDT-WR-dp is used for the Dif-PDT-WR case. Finally, Fig. 2 summarizes the various strategies that are investigated in this study.
VII. TIME COMPLEXITY ANALYSIS PROPOSED ALGORITHMS
OF
This section analyzes the time complexity of the presented algorithms for provisioning a random connection
Fig. 2. Summary of the provisioning strategies.
request. For this purpose we use the notations already defined in Section IV along with some new notation. The time to compute k-shortest paths for the connection request is OkE V log V. While the time to perform wavelength assignment using k-shortest paths is OLWk. The PDT-WoR provisions a connection request when a departure event occurs in the network. Thus, it requires a total time of OkE V log V LWkd, where d is the number of connection departures from the network before the request gets connected. The PDT-WR takes into account the network state for the td time interval, computes the set D of all connections that will be released within td , and reserves in advance the required resources for the connection request. Therefore, the total time required for PDT-WR is OkE V log V LWkD. Similarly, Dif-PDT-WoR gives priority to connections with low td in the rescheduling queue. The connection requests can be arranged offline in the queue according to their td by using a sorting algorithm of complexity equal to OQ log Q, where Q is the length of the rescheduling queue. The Dif-PDT-WR releases and reschedules the reserved resources of SC-II and SC-III if required for SC-I connection requests. Hence the Dif-PDT-WR time requirements for SC-I connections will be OkE V log V LWkD Y, where Y indicates the number of connection requests of SC-II and SC-III, whose reserved resources are rescheduled in favor of SC-I requests. Finally, the time requirement for Dif-PDT-{WoR/ WR}-wr and Dif-PDT-{WoR/WR}-dp are the same as that for Dif-PDT-{WoR/WR}, except they utilize different values of W and k for connections belonging to different SCs, as described in Subsections VI.B and VI.C. It can be observed that by increasing the network size (i.e., the value of V) the number of network links E and the path length (i.e., value of L) will also increase. Naturally, the network size will be a dominant parameter in the time complexity of the presented algorithms.
VIII. NUMERICAL RESULTS We custom built a discrete-event-driven simulator and evaluated the proposed strategies using network topologies with different nodal degrees, as shown in Figs. 3 and 4.
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BP
FOR
TABLE II DIFFERENT ALGORITHMS
Load Strategy
Fig. 3. EON network topology with 30 nodes and 48 links.
Figure 3 depicts the European backbone optical network (EON) topology with nodal degree 3.2, while Fig. 4 presents a modified EON topology with nodal degree 4. The network links are assumed to be bidirectional with the same number of wavelengths, W 16, in both directions. It is further assumed that the network is not equipped with wavelength converters; thus a connection request must use the same wavelength from the source to the destination node. Connections are assumed to arrive in the network according to a Poisson process with connection th following an exponential distribution. The average th is normalized to unity and the different values of the offered network load are obtained by increasing the arrival rate and keeping the mean th constant. Thus, the offered network traffic load in Erlangs equals the arrival rate. Furthermore, for the results shown here, we simulate different sets of connection requests, which are uniformly distributed among all node pairs. Each plotted point has a 95% confidence level, with the interval not larger than 5% of the plotted value except in the case of very small BP. For Dif-PDT-{WoR/WR}-dp the values of u and v are set to 6 and 3, respectively. The value of T (shown in Table III) for Dif-PDT-{WoR/WR}-wr is adjusted according to the network traffic distribution among the SCs
NDT PDT-WoR Dif-PDT-WoR Dif-PDT-WoR-wr Dif-PDT-WoR-dp PDT-WR Dif-PDT-WR Dif-PDT-WR-wr Dif-PDT-WR-dp NDT PDT-WoR Dif-PDT-WoR Dif-PDT-WoR-wr Dif-PDT-WoR-dp PDT-WR Dif-PDT-WR Dif-PDT-WR-wr Dif-PDT-WR-dp NDT PDT-WoR Dif-PDT-WoR Dif-PDT-WoR-wr Dif-PDT-WoR-dp PDT-WR Dif-PDT-WR Dif-PDT-WR-wr Dif-PDT-WR-dp
Blocking Probability
(Erlangs)
SC-I
SC-II
SC-III
70 70 70 70 70 70 70 70 70 100 100 100 100 100 100 100 100 100 130 130 130 130 130 130 130 130 130
0.00094 0.00104 0.00098 0.00044 0.00020 0.00090 0.00089 0.00030 0.00016 0.00959 0.01219 0.01106 0.00815 0.00375 0.01092 0.01039 0.00595 0.00297 0.02515 0.03665 0.03271 0.02379 0.01630 0.03458 0.03118 0.02094 0.01272
0.00094 0.00017 0.00016 0.00039 0.00017 0.00013 0.00011 0.00034 0.00009 0.00959 0.00242 0.00192 0.00508 0.00245 0.00184 0.00151 0.00326 0.00137 0.02515 0.00764 0.00527 0.02060 0.01035 0.00609 0.00471 0.00903 0.00466
0.00094 0.00004 0.00005 0.00032 0.00088 0.00003 0.00008 0.00036 0.00008 0.00959 0.00046 0.00056 0.00340 0.00238 0.00035 0.00201 0.00546 0.00208 0.02515 0.00155 0.00172 0.01056 0.00820 0.00126 0.00895 0.01931 0.00941
and the network nodal degree. All the proposed strategies are compared with a basic approach, i.e., with the connection provisioning with no delay tolerance (NDT). The probability of rejection of connection (BP) requests and average link load are used to evaluate the performance of the proposed strategies. To illustrate the performance of presented strategies more clearly under different traffic load cases, the BP results are normalized to the NDT BP value. However, Table II provides the actual BP values for these strategies. Note that all the values in Table II and the results that are shown in the figures are simulated for the EON topology (Fig. 3) with equally distributed traffic among the SCs, unless otherwise mentioned.
A. Comparison of BP for Different Traffic Class Distribution
Fig. 4. Network topology with 30 nodes and 60 links.
Figure 5 exhibits the normalized values of total and SC-I BP for PDT-WoR. These BP values are obtained for three different traffic load and traffic mix cases where 1, 2, and 3 in the figure denote the different percentage of connection arrivals from SC-I, SC-II, and SC-III, respectively. The figure shows that when a td and th aware approach is used the total network BP is significantly reduced for all traffic loads and traffic mixes. However, the BP of SC-I is exacerbated compared to NDT, and this difference becomes more obvious when the network load and the share of SC-II and SC-III in total network traffic increases.
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Fig. 5. Normalized blocking probability versus network load.
Fig. 7. Normalized blocking probability versus network load.
To examine the impact of high td of SC-III over the BP performance of SC-I and SC-II, Fig. 6 shows the normalized BP of all three SCs for uniform traffic when the td ∕th ratio for SC-III is elevated from 3 to 6. The figure reveals that the BP of SC-I is further aggravated by increasing the td ∕th ratio of SC-III, while SC-II still manages to keep its BP lower than that of the NDT case. This means that SC-II, which is in between SC-I and SC-III (in terms of td ∕th ratio), is unaffected by the high ratio of SC-III. Moreover, the results in Figs. 5 and 6 confirm that high td of SC-II and SC-III connection requests elevates their provisioning success rates. Consequently, SC-II and SC-III occupy most of the network resources, leaving scarce resources for the less delay tolerant connection requests of SC-I. Thus, additional promoting strategies for SC-I requests are required despite the fact that td and th awareness can help to reduce total network BP.
PDT-WoR, while the schemes demonstrated in Fig. 8 are the adjusted versions of PDT-WR. All these strategies are adapted to facilitate the provisioning of low delay tolerant requests. Several insights can be gained from these results.
The performance results of the proposed strategies, which aim at enhancing the provisioning success rate of requests with smaller td, are shown in Figs. 7 and 8. The techniques shown in Fig. 7 are the modified versions of
Normalized blocking probability
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Second, only the algorithms that use the wavelength reservation (Dif-PDT-{WoR/WR}-wr) and different path set (Dif-PDT-{WoR/WR}-dp) techniques achieve the stated objective, that is, to reduce BP for all SCs including SC-I 1.4
Normalized blocking probability
B. 2 BP Comparison of Service Differentiated Strategies
First, the algorithms based on the PDT-WR concept (i.e., Fig. 8) exhibit better performance compared to the ones that adopt the PDT-WoR technique (i.e., Fig. 7). The performance difference between PDT-WoR and PDT-WR for all three SCs can be observed more clearly from the BP values given in Table II. This reveals that the strategy of reserving resources in advance in PDT-WR by exploiting the th information provides better BP performance for all SCs compared to PDT-WoR. Although not shown in the figure, for a traffic mix of 60∶20∶20 among SC-I, SC-II, and SC-III, respectively, an average gain of 5% and 7% in terms of reduced BP are observed for Dif-PDT-WR-wr and Dif-PDT-WR-dp compared to Dif-PDT-WoR-wr and DifPDT-WoR-dp, respectively. These gain values elevate to 12% and 14%, respectively, for a traffic mix of 20∶40∶40 among the SCs.
1: Dif−PDT−WR 2: Dif−PDT−WR−wr 1.2 3: Dif−PDT−WR−dp
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compared to NDT. The priority in the rescheduling queue (i.e., Dif-PDT-WoR and Dif-PDT-WR) diminishes the degrading performance of SC-I compared to PDT-WoR and PDT-WR; however, the negative effect of higher td in SC-II and SC-III over SC-I cannot be mitigated solely by giving priority to SC-I connection requests. Third, the algorithms that employ different path sets (Dif-PDT-{WoR/WR}-dp) show better performance compared to the wavelength reservation (Dif-PDT-{WoR/WR}wr) algorithm. This is because of the following two factors. First, Dif-PDT-{WoR/WR}-dp does not reserve wavelengths on the network links for SC-I unlike Dif-PDT-{WoR/WR}wr, which improves the BP of SC-II and SC-III connection requests. Second, Dif-PDT-{WoR/WR}-dp utilizes larger path sets for SC-I, which enhances the search space and thus improves the probability for SC-I connection requests to find free paths and get connected.
C. BP Comparison of Different SCs To examine the BP performance of different SCs for the proposed algorithms, Fig. 9 displays the BPs of the different SCs for Dif-PDT-{WoR/WR}-wr. Different observations can be made from the results shown in the figure.
1
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Dif-PDT-WoR-wr for nodal degree 3.2 Dif-PDT-WoR-wr for nodal degree 4.0 Dif-PDT-WR-wr for nodal degree 3.2 Dif-PDT-WR-wr for nodal degree 4.0
3 2 2 2
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2 1 1 1
Similarly, the same trend as described above is observed for Dif-PDT-{WoR/WR}-dp algorithms whose BP values are given in Table II.
D. Impact of Threshold Value for Dif-PDT-{WoR/ WR}-wr Table III exhibits the number (T) of wavelengths required to be reserved on the network links in order to keep the BP of SC-I below or equal to that for NDT. The required wavelengths depend on the traffic distribution among SCs and the network nodal degree. For a larger share of SC-I traffic more wavelengths are required to balance the adverse effect of the high td of SC-II and SC-III. Similarly, a less connected network requires more wavelength reservation compared to a densely connected network.
E. Average Link Load Figure 10 shows the total average link load in order to highlight the effect of the presented algorithms on the utilization of network resources. For NDT the link utilization is below 50% because it blocks the highest number of connections from all the SCs. However, as Dif-PDT-WoR-dp searches on high number paths for SC-I connection requests, it exploits the idle available resources for SC-I requests. However, by not reserving wavelengths on the network links for SC-I the Dif-PDT-WoR-dp improves the BPs of SC-II and SC-III. Thus the total link utilization for the Dif-PDT-WoR-dp strategy increases significantly compared to NDT, e.g., from 12% to 15% when the connection 0.55
Dif−PDT−WoR−dp Dif−PDT−WoR−wr Dif−PDT−WoR NDT
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TABLE III DIF-PDT-{WOR/WR}-WR ALGORITHMS
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First, both SC-I and SC-II demonstrate better performance for Dif-PDT-WR-wr compared to Dif-PDT-WoR-wr, whereas SC-III shows better results for Dif-PDT-WoR-wr. This is because SC-III has the lowest priority and the only chance it can grab resources in competition with SC-I and SC-II stems from its higher td ; hence, it can outperform SC-I and SC-II when the resources are not reserved for the prioritized classes. Second, for the Dif-PDT-WR-wr algorithm at medium and high loads, SC-II achieves the best BP performance compared to SC-I and SC-III. This is because the high td of SC-II enables it to surpass SC-I, while its priority in the rescheduling queue over SC-III allows it to reserve resources before SC-III requests, thus improving its BP compared to SC-III. This indicates that the gain in terms of network connection provisioning, attained by any SC, will depend not only on its td value but also on its priority level in the rescheduling queue.
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Fig. 9. Normalized blocking probability versus network load.
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Fig. 10. Average link load versus network load.
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certain line rate) and applying our methodology separately to the services belonging to the specific line rates. NDT 1 2
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arrival rate varies from 110 to 130 Erlangs. Moreover, the total link utilization for the other strategies, i.e., Dif-PDT-WoR-wr and Dif-PDT-WoR, also improves compared to NDT.
F. Impact of Network Connectivity on BP The simulation experiments were carried out using the network topology shown in Fig. 4 in order to evaluate the impact of network connectivity on the performance of the proposed algorithms. It is shown that the network BP improves further for the topology with higher nodal degree. Similarly, the negative impact of high td of SC-II and SC-III on the provisioning rate of SC-I is reduced, i.e., for PDT-WoR the degradation in SC-I BP drops to 20%, 18%, and 9% from 37%, 31%, and 18% when SC-I constitutes 60%, 33.34%, and 20% in the total traffic, respectively. Consequently, the value of T for DIF-PDT-WoR-wr decreases for a highly connected network, as shown in Table III. Furthermore, the gain of the differentiation algorithms based on the PDT-WR concept (i.e., Dif-PDT-WR{wr/dp}) compared to the ones that employ PDT-WoR (i.e., Dif-PDT-WoR-{wr/dp}) enhances for the network topology shown in Fig. 4. Finally, Fig. 11 demonstrates the performance of the proposed strategies that exploit the PDT-WR scheme. The figure confirms the same trend as witnessed in Fig. 8. However, the Dif-PDT-WR-{wr/dp} shows high improvement in BP compared to the performance observed for EON in Fig. 8.
IX. CONCLUSION In this work we studied the impact of quality of service differentiation by putting emphasis on time-based service level specifications such as td and connection th . We have applied our novel dynamic provisioning strategies on WDM networks, with uniform line rates assigned to each wavelength. The work can be extended to address optical networks supporting mixed line rates by dividing services according to the requested bandwidth (corresponding to a
When networks with several SCs with different td are examined, the improvement in BP of high td SCs ends up in an increase in the BP of a low td SC. To eliminate this adverse effect on the provisioning rate of SCs with low td, this paper proposes strategies that balance the provisioning rate among the different classes. The proposed strategies combine in a different way the concept of td and connection th awareness and are specifically tailored to facilitate the provisioning of the most stringent (in terms of td ) SC. Moreover, the impact of network nodal degree, load, and network traffic distribution among the SCs on the performance of the proposed strategies was investigated. Among the proposed strategies, the ones based on the reservation of a fraction of available resources and extra paths along with priority in the rescheduling queue were successful in eliminating the adverse effect of high delay tolerant classes on the BP performance of the SC with low delay tolerance. These strategies can be used even without the queue priority for the low td SC, but at the expense of enhancing the reserved resources and extra path sets. The strategy of giving priority only to low delay tolerant class requests in the rescheduling queue can reduce this adverse effect, although it cannot overcome the degradation completely. Conclusively, these results highlight that it is important to select a strategy that avoids the risk that network resources are grabbed by a specific SC.
ACKNOWLEDGMENTS The work described in this paper was carried out with the support of the “Security in All-Optical Network” project, funded by VINNOVA (The Swedish Governmental Agency for Innovation Systems).
REFERENCES [1] R. Ramaswami and K. Sivarajan, “Routing and wavelength assignment in all-optical networks,” IEEE/ACM Trans. Netw., vol. 3, pp. 489–500, Oct. 1995. [2] L. Gutierrez, P. Garfias, M. Andrade, C. Pastor, and S. Sallent, “Next generation optical access networks from TDM to WDM,” Trends in Telecommunications Technologies, Mar. 2010 [Online]. Available: http://www.intechopen .com/books/trends‑in‑telecommunications‑technologies/next‑ generation‑optical‑access‑networks‑from‑tdm‑to‑wdm. [3] H.-K. Lee, H.-S. Cho, J.-Y. Kim, and C.-H. Lee, “A WDM-PON with an 80 Gb/s capacity based on wavelength-locked Fabry-Perot laser diode,” Opt. Express, vol. 18, no. 17, pp. 18077–18085, Aug. 2010. [4] C. Pinart and G. Giralt, “On managing optical services in future control-plane-enabled IP/WDM networks,” J. Lightwave Technol., vol. 23, no. 10, pp. 2868–2876, Oct. 2005. [5] P. C. Fishburn and A. M. Odlyzko, “Dynamic behavior of differential pricing and quality of service options for the Internet,” in Proc. 1st Int. Conf. on Information and Computation Economies, 1998, pp. 128–139. [6] L. He and J. Walrand, “Pricing differentiated Internet services,” in Proc. IEEE 24th Annu. Joint Conf. of the IEEE
1260 J. OPT. COMMUN. NETW./VOL. 5, NO. 11/NOVEMBER 2013
Muhammad et al.
Computer and Communications Societies, vol. 1, Mar. 2005, pp. 195–204.
optical networks,” in IEEE Int. Conf. on Communications (ICC), 2002, vol. 5, pp. 2722–2726.
[7] W. Fawaz, B. Daheb, O. Audouin, M. Du-Pond, and G. Pujolle, “Service level agreement and provisioning in optical networks,” IEEE Commun. Mag., vol. 42, no. 1, pp. 36–43, Jan. 2004.
[23] S. Figueira, N. Kaushik, and S. Naiksatam, “Advance reservation of lightpaths in optical-network based grids,” in IEEE Conf. on Broadnets/Gridnets, 2004.
[8] W. Fawaz, I. Ouaiss, K. Chen, and H. Perros, “Deadline-based connection setup in wavelength-routed WDM networks,” Comput. Netw., vol. 54, no. 11, pp. 1792–1804, Aug. 2010.
[24] T. D. Wallace and A. Shami, “Connection management algorithm for advance lightpath reservation in WDM networks,” in Fourth Int. Conf. on Broadband Communications, Networks and Systems (BROADNETS), Sept. 2007, pp. 837–844.
[9] W. Fawaz, A. Sinno, R. Bardan, and M. Khabbaz, “A novel event-driven QoS-aware connection setup management scheme for optical networks,” in Digital Information and Communication Technology and Its Applications (Communications in Computer and Information Science, Vol. 167). Berlin: Springer, 2011, pp. 139–150. [10] W. Ni, X. Zheng, C. Zhu, Y. Li, Y. Guo, and H. Zhang, “Achieving resource-efficient survivable provisioning in service differentiated WDM mesh networks,” J. Lightwave Technol., vol. 26, no. 16, pp. 2831–2839, Aug. 2008. [11] B. Kantarci, E. Al-Sukhni, J. Sarker, and H. Mouftah, “Performance analysis of SLA-aware connection provisioning in optical networks,” in 13th Int. Conf. on Transparent Optical Networks (ICTON), June 2011, pp. 1–4. [12] C. Cavdar, M. Tornatore, F. Buzluca, and B. Mukherjee, “Shared-path protection with delay tolerance (SDT) in optical WDM mesh networks,” J. Lightwave Technol., vol. 28, no. 14, pp. 2068–2076, July 2010. [13] A. Iselt, A. Kirstädter, and R. Chahine, “The role of ASON and GMPLS for the bandwidth trading market,” in Proc. 1st Int. Conf. E-Business and Telecommunication Networks, Aug. 2004, pp. 306–311. [14] R.-J. Essiambre, G. Kramer, P. Winzer, G. Foschini, and B. Goebel, “Capacity limits of optical fiber networks,” J. Lightwave Technol., vol. 28, no. 4, pp. 662–701, Feb. 2010. [15] C. Cavdar, F. Buzluca, M. Tornatore, and B. Mukherjee, “Dynamic scheduling of survivable connections with delay tolerance in WDM networks,” in IEEE INFOCOM Workshops, Apr. 2009. [16] A. Muhammad, R. Forchheimer, and L. Wosinska, “Impairment-aware dynamic provisioning in WDM networks with set-up delay tolerance and holding-time awareness,” in 17th Int. Conf. on Networks (ICON), Dec. 2011. [17] M. Tornatore, A. Baruffaldi, H. Zhu, B. Mukherjee, and A. Pattavina, “Exploiting connection-holding time for an efficient dynamic traffic grooming,” in 6th Int. Workshop on Design and Reliable Communication Networks (DRCN), Oct. 2007. [18] D. Lucerna, A. Baruffaldi, M. Tornatore, and A. Pattavina, “On the efficiency of dynamic routing of connections with known duration,” in IEEE Int. Conf. on Communications (ICC), June 2009. [19] Y. Zhu, G. N. Rouskas, and H. G. Perros, “A comparison of allocation policies in wavelength routing networks,” Photonic Network Commun., vol. 2, pp. 267–295, 2000. [20] A. Muhammad, C. Cavdar, L. Wosinska, and R. Forchheimer, “Effect of delay tolerance in WDM network with differentiated services,” in Optical Fiber Communication Conf. (OFC), Mar. 2011. [21] J. Zheng and H. Mouftah, “Supporting advance reservations in wavelength-routed WDM networks,” in Proc. 10th Int. Conf. on Computer Communications and Networks, 2001, pp. 594–597. [22] J. Zheng and H. T. Mouftah, “Routing and wavelength assignment for advance reservation in wavelength-routed WDM
[25] T. D. Wallace, A. Shami, and C. Assi, “Advance lightpath reservation for WDM networks with dynamic traffic,” J. Opt. Netw., vol. 6, no. 7, pp. 913–924, July 2007. [26] D. Andrei, M. Batayneh, C. Martel, and B. Mukherjee, “Provisioning of deadline-driven requests with flexible transmission rates in different WDM network architectures,” in Optical Fiber Communication Conf. (OFC), Feb. 2008. [27] D. Andrei, M. Tornatore, D. Ghosal, C. Martel, and B. Mukherjee, “On-demand provisioning of data-aggregation sessions over WDM optical networks,” J. Lightwave Technol., vol. 27, no. 12, pp. 1846–1855, June 2009. [28] D. Andrei, M. Batayneh, S. Sarkar, C. Martel, and B. Mukherjee, “Deadline-driven bandwidth allocation with flexible transmission rates in WDM networks,” in IEEE Int. Conf. on Communications (ICC), May 2008, pp. 5354–5358. [29] D. Andrei, M. Tornatore, M. Batayneh, C. Martel, and B. Mukherjee, “Provisioning of deadline-driven requests with flexible transmission rates in WDM mesh networks,” IEEE/ACM Trans. Netw., vol. 18, pp. 353–366, Apr. 2010. [30] X. Yang, L. Shen, A. Todimala, B. Ramamurthy, and T. Lehman, “An efficient scheduling scheme for on-demand lightpath reservations in reconfigurable WDM optical networks,” in Optical Fiber Communication Conf. (OFC), Mar. 2006, paper OTuN. [31] L. Shen, A. Todimala, B. Ramamurthy, and X. Yang, “Dynamic lightpath scheduling in next-generation WDM optical networks,” in Proc. 25th IEEE Int. Conf. on Computer Communications (INFOCOM), Apr. 2006. [32] L. Shen, X. Yang, A. Todimala, and B. Ramamurthy, “A twophase approach for dynamic lightpath scheduling in WDM optical networks,” in IEEE Int. Conf. on Communications (ICC), June 2007, pp. 2412–2417. [33] S. Tanwir, L. Battestilli, H. Perros, and G. Karmous-Edwards, “Dynamic scheduling of network resources with advance reservations in optical grids,” Int. J. Netw. Manage., vol. 18, no. 2, pp. 79–105, Mar. 2008. [34] N. Kaushik, S. Figueira, and S. Chiappari, “Flexible timewindows for advance reservation scheduling,” in 14th IEEE Int. Symp. on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Sept. 2006, pp. 218–225. [35] M. Tornatore, A. Baruffaldi, H. Zhu, B. Mukherjee, and A. Pattavina, “Holding-time-aware dynamic traffic grooming,” IEEE J. Sel. Areas Commun., vol. 26, no. 3, pp. 28–35, Apr. 2008. [36] A. Munir, S. Tanwir, and S. Zaidi, “Holding time aware dynamic bandwidth allocation algorithm for emerging bandwidth on demand multicast applications,” in 6th Int. Symp. on High-Capacity Optical Networks and Enabling Technologies (HONET), Dec. 2009, pp. 16–21. [37] E. Al Sukhni and H. Mouftah, “Distributed holding-timeaware shared-path-protection provisioning framework for optical networks,” in IEEE Symp. on Computers and Communications (ISCC), July 2009, pp. 730–735.
Muhammad et al.
VOL. 5, NO. 11/NOVEMBER 2013/J. OPT. COMMUN. NETW. 1261
[38] J. de Santi, A. Drummond, N. da Fonseca, and A. Jukan, “Load balancing for holding-time-aware dynamic traffic grooming,” in IEEE Global Telecommunications Conf. (GLOBECOM), Dec. 2010.
[41] A. Bouch, A. Kuchinsky, and N. Bhatti, “Quality is in the eye of the beholder: Meeting users’ requirements for Internet quality of service,” Tech. Rep. HPL-2000-4, HP Labs, Palo Alto, CA, Jan. 2000.
[39] D. Lucerna, M. Tornatore, B. Mukherjee, and A. Pattavina, “Dynamic routing of connections with known duration in WDM networks,” in IEEE Global Telecommunications Conf. (GLOBECOM), Dec. 2009.
[42] M. Naldi, “A mixture model for the connection holding times in the video-on-demand service,” Perform. Eval., vol. 47, pp. 23–41, Jan. 2002.
[40] H. B. Newman, M. H. Ellisman, and J. A. Orcutt, “Data intensive e-science frontier research,” Commun. ACM, vol. 46, pp. 68–77, 2003.
[43] S. Sen, J. Rexford, J. Dey, J. Kurose, and D. Towsley, “Online smoothing of variable-bit-rate streaming video,” IEEE Trans. Multimedia, vol. 2, no. 1, pp. 37–48, Mar. 2000.
